370 research outputs found
Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper)
Get PDFThis paper explores the usefulness of Twitter data to detect traffic events and their geographical locations in India through machine learning and NLP. We develop a classification module that can identify tweets relevant for traffic authorities with 0.80 recall accuracy using a Naive Bayes classifier. The proposed model also handles vernacular geographical aspects while retrieving place information from unstructured texts using a multi-layered georeferencing module. This work shows Mumbai has a wide spread use of Twitter for traffic information dissemination with substantial geographical information contributed by the users
Towards urban mobility-based activity knowledge discovery: interpreting motion trajectories
Get PDF© 2017 Dr. Rahul Deb DasUnderstanding travel behaviour is important for an effective urban planning and to enable different context-aware mobility service provisions. To this end, it is essential to model different mobility-based activities in available trajectory data. However, the semantics of activity varies from context to context, which poses a challenge for developing a connected knowledge flow for different services.
Currently, such mobility-based information is typically collected through manual paper-based surveys. These surveys preserve context, but come with their own inherent quality issues, and are expensive in comparison to data analytics methods. To address this issue this research leverages the emerging concept of smartphone-based travel surveys that collect people’s movement behaviour in terms of raw trajectories.
This research proposes an ontological framework that can model activities in a hierarchical manner adapting to different contexts and thereby addressing the challenges of trajectory data analytics mentioned above. This research also explores how raw trajectories collected by a smartphone can be interpreted to generate mobility information (e.g., transport modes, trips). While interpreting the trajectories this thesis models uncertainties that may exist during people’s travel behaviour and interpretation process.
In this research, a particular focus is given to knowledge representation, that is understanding urban movement behaviour from detecting transport modes in trajectories. One presented form of knowledge representation is a fuzzy logic based approach to mode detection. The knowledge representation is essential to extract semantics related to a given activity. This research also introduces the concept of near-real time mode detection and investigates the performance of a purely knowledge-driven model works effectively in a near-real time scenario. Since a knowledge-driven model at different temporal granularities while detecting a given transport mode. The knowledge-driven model that works in offline, typically requires kinematic features computed over sufficiently long segments. But in near-real time these segments must be shorter and requires the model to be adaptive. To address this issue a machine learning based model has been deployed, which can learn from the historical data, and work in varied conditions. But machine learning models work as a black box and cannot explain their reasoning scheme owing to a semantic gap in the activity knowledge base. On the other hand, a fuzzy logic based model can explain its reasoning scheme but cannot adapt to varying conditions. To bridge the trade-off between these approaches this research proposes a hybrid knowledge-driven framework that is capable of self-adaptation and explaining its reasoning scheme. The results show the hybrid model performs better than a purely knowledge-driven model and works at par with the machine learning models for transport mode detection. This research also justifies a hybrid approach can model the activity in a consistent and adaptive manner while explaining the semantics related to different mobility-based activities.
In this research different uncertainties related to a motion trajectory interpretation process have been addressed. A particular focus is given on modelling the temporal uncertainties that exist between predicted, scheduled and reported trips. Such a temporal uncertainty quantification measures the reliability (or uncertainty) in an inference process in the interest of information retrieval at different contexts. Considering the lack of semantics in GPS trajectories an investigation is also made whether incorporating low sampled IMU information in addition to a GPS trajectory can improve the accuracy. This research also identifies existing trajectory segmentation approaches (e.g., clustering-based or walking-based approaches) are subjective and thus lacks adaptivity. In order to address these issues a novel state-based bottom-up trajectory interpretation model is developed, which can generate mobility information at different temporal granularities. The model also demonstrates its efficacy, flexibility, and adaptivity over the existing top-down approaches This research also demonstrates that using a GPS trajectory, it is possible to generate modal state information comparatively at a coarser granularity but shorter than the time required to generate information from a historical GPS trajectory. The response time is subject to a particular application domain.
The research presented in this thesis has a potential to improve the background intelligence in smartphone-based travel surveys and smartphone-based travel applications facilitating mobility-based context-aware service provisions where the notion of activity is prevalent at different granularities. However, this research cannot distinguish composite activities, which require future work. With the emergence of Web 2.0 and ubiquitous location sensing technologies, the location information can come from various sources with the different level of inaccuracies and space-time granularities. The models developed in this research currently work best on GPS trajectories sampled at 1 Hz to 2 Hz frequency, which may be enriched with IMU information. However, the models need some adjustments and incorporations of additional features and rules when the location information comes not only from GPS but also from GSM, Wi-Fi, smart-card. The models developed in this research are flexible, transparent and offer provisions for further enrichment of raw trajectories and extract finer activity information. This research has a potential to understand mobility patterns at an aggregate and a disaggregate level, and thereby serve different application domains e.g., personalized activity recommendations during a travel, emergency service provisions, real-time traffic management and long term urban policy making
Understanding Users’ Satisfaction towards Public Transit System in India: A Case-Study of Mumbai
No full textIn this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score
Assessing the potential of social media for estimating recreational use of urban and peri-urban forests
Get PDFAcknowledgements The research for this paper was financially supported through the Swiss Federal Office for the Environment (FOEN). The views and opinions expressed in this paper are those of the authors, and do not necessarily represent the policies or official positions of the FOEN or the institutions they work for. We thank Rahul Deb Das for his assistance in data collection and processing. We gratefully acknowledge the comments and feedback of two anonymous reviewers.Peer reviewe
A context-sensitive conceptual framework for activity modeling
Get PDFHuman motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place "along the way," such as "reading on the bus," are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., "holidaying in a country," could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts
NHS Breast Screening multidisciplinary working group guidelines for the diagnosis and management of breast lesions of uncertain malignant potential on core biopsy (B3 lesions).
No full textAuthor(s) Pre or Post Print CopyNeedle core biopsy is considered the histological diagnostic method of choice for screen-detected breast lesions. Although the majority are definitively diagnosed as normal, benign, or malignant, approximately 7% are categorised as B3, of uncertain malignant potential. These include a wide range of lesions with different risks of associated malignancy from <2% to approaching 40% from literature review in UK practice. Historically, these have typically been surgically excised as a diagnostic procedure but the majority are then proven to be benign. An alternative approach, for many of these lesions, is thorough sampling/excision by vacuum-assisted biopsy techniques to exclude the presence of co-existing carcinoma. This would potentially reduce the benign open biopsy rate whilst maintaining accuracy of cancer diagnosis. A group from the Radiology, Surgery, and Pathology NHS Breast Screening Programme Co-ordinating Committees and an additional co-opted expert were charged with review and development of guidelines for the clinical management of B3 lesions. The guidelines reflect suggested practice as stated by the NHS Breast Screening Programme and approved by the Royal College of Radiologists
A fuzzy logic based transport mode detection framework in urban environment
No full textTransport mode detection is an emerging research area in different domains such as urban planning, context-aware mobile computing, and intelligent transportation systems. Current approaches are mostly data-driven, based on machine learning approaches. However, machine learning models require substantial training data and cannot explain the reasoning procedure. Data-driven approaches also fall short while interpreting trajectories where ground truth information is limited. Therefore, this paper develops a novel knowledge-based approach for interpreting smartphone global positioning system trajectories by detecting various transport modes used during travel. The proposed model is based on an expert system that can work without any training, based solely on expert knowledge. Core is a fuzzy multiple-input multiple-output expert system using kinematic and spatial information with a well explained fuzzy reasoning scheme through a fuzzy rule base. The model can provide alternate predictions with varied certainty factors. Different membership function combinations have been evaluated in terms of accuracy and ambiguity, and the result demonstrates that the model performs best using a Gaussian–Gaussian combination, comparable to the existing machine learning approaches
Accuracy of classification of invasive lobular carcinoma on needle core biopsy of the breast.
Get PDFAlthough the UK National Institute for Health and Care Excellence guidelines recommend that in patients with biopsy-proven invasive lobular carcinoma (ILC), preoperative MRI scan is considered, the accuracy of diagnosis of ILC in core biopsy of the breast has not been previously investigated. Eleven pathology laboratories from the UK and Ireland submitted data on 1112 cases interpreted as showing features of ILC, or mixed ILC and IDC/no special type (NST)/other tumour type, on needle core biopsy through retrieval of histology reports. Of the total 1112 cases, 844 were shown to be pure ILC on surgical excision, 154 were mixed ILC plus another type (invariably ductal/NST) and 113 were shown to be ductal/NST. Of those lesions categorised as pure ILC on core, 93% had an element of ILC correctly identified in the core biopsy sample and could be considered concordant. Of cores diagnosed as mixed ILC plus another type on core, complete agreement between core and excision was 46%, with 27% cases of pure ILC, whilst 26% non-concordant. These data indicate that there is not a large excess of expensive MRIs being performed as a result of miscategorisation histologically. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ DOI: 10.1136/jclinpath-2016-203886 PMID: 27510520 [PubMed - as supplied by publisher] 2. Curr Opin Nephrol Hypertens. 2015 Nov;24(6):511-6. doi: 10.1097/MNH.0000000000000168. Acute kidney: improving the pathway of care for patients and across healthcare. Fluck RJ(1). Author information: (1)Department of Renal Medicine, Royal Derby Hospital, Derby, United Kingdom. PURPOSE OF REVIEW: Acute kidney injury (AKI) is common, harmful and of global concern. There is a need to understand the pathway of the management of AKI in order to identify potential areas where care can be improved, for the individual and for healthcare systems. RECENT FINDINGS: There has been considerable focus on risk assessment and earlier detection using changes in serum creatinine. There is less understanding of optimal management, enhanced and long-term recovery, and education to support better care. Using Kidney Disease Improving Global Outcomes-based criteria to improve the detection of AKI improves its detection, but requires supportive training and education to deliver better outcomes.Policy makers need to understand the personal and economic burden that results from AKI. There is a need to provide commissioning support, improvement methodologies, and registry initiatives with research investment to sustain progress in overall management. SUMMARY: There is clear evidence of harm related to AKI and a need to improve the reliability of care. The prevalence is high, with the potential to significantly improve short-term and long-term care by addressing all the elements in the pathway, at both patient and system level, assessing risk, detection, treatment, and recover
Geospatial data science approaches for transport demand modeling
No full textModeling transport-the phenomenon of people or goods moving in vehicles in space and time and being constrained in that movement by transport networks-is inherently related to geospatial data (Miller and Shaw 2001, 2015). People, goods, and vehicles are located somewhere at any time, in relation to each other as well as in relation to various mode-specific transport networks; they are coming from some location and heading to another location within some time constraints. The movements of people and goodscollectively defining the transport demand-can be solitary or shared, but are neither independent of each other, nor independent of the vehicles, which are defining the transport supply. In addition to the factors of space and time, economic, social, and individual factors also determine choice and behavior. That is why transport has long been recognized as a complex system and, as such, hard to model
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